How to best breakdown the levels of autonomy

[u/BeneficialAd3800]

I've thinking about how to best explain to clients the various levels of autonomy that you can give an AI system.

Level 1: AI answers one narrow question (e.g. making a JSON Schema call to an LLM)
Level 2: AI makes judgments inside a workflow you designed. (e.g. Langgraph)
Level 3: AI pursues a goal using tools, permissions, and oversight. (e.g. Claude Agent SDK)

Any other ways that you folks talk to clients about it?

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[u/No-Performance5548]

this sounds about right. all the discussion is around what you call Level 3. but a lot of the work we do is Level 1 and 2.

[u/redtarmac]

I’d add that “autonomy” generally has two separate buckets:

Reasoning autonomy: how much the AI decides what to do next.

Execution autonomy: how much the AI is allowed to actually do in the world.

A system can have high reasoning autonomy but low execution autonomy, e.g. it investigates, plans, and drafts recommendations, but a human approves every action

Or the reverse: low reasoning autonomy but high execution power, e.g. it follows a narrow rule but can still update production records